Artificial intelligence based problem descriptions

ABSTRACT

Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.

BACKGROUND

One or more embodiments relate to providing problem descriptions, andmore specifically, to providing problem descriptions using artificialintelligence technology.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the disclosure. This summary is not intended toidentify key or critical elements, or to delineate any scope ofparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusesand/or computer program products that can autonomously provide problemdescriptions using artificial intelligence technology are described.

According to an embodiment, a system is provided. The system can includea memory that stores computer executable components. The system can alsoinclude a processor, operably coupled to the memory, and that canexecute the computer executable components stored in the memory. Thecomputer executable components can include: a query component thatgenerates key performance indicators from a query, determines a subsetof key performance indicators that individually have a performance belowa threshold, and maps the subset of key performance indicators tooperational metrics. The computer executable components can furtherinclude a learning component that generates, using artificialintelligence, problem descriptions from one or more of the subset of keyperformance indicators or the operational metrics and transmits theproblem descriptions to a first database. The computer executablecomponent can also include a content component that retrieves theproblem descriptions from the first database, searches a second databasefor a recommendation based on the problem descriptions, and provides therecommendation for an entity.

According to one or more example embodiments, a computer-implementedmethod is provided. The computer-implemented method includes:generating, by a query component operatively coupled to a processor, keyperformance indicators from a query, determining a subset of keyperformance indicators that individually have a performance below athreshold, and mapping the subset of key performance indicators tooperational metrics. The method further includes generating, by alearning component operatively coupled to the processor, usingartificial intelligence, problem descriptions from one or more of thesubset of key performance indicators or the operational metrics andtransmitting the problem descriptions to a first database. The methodincludes retrieving, by a content component operatively coupled to theprocessor, the problem descriptions from the first database, searching asecond database for a recommendation based on the problem descriptions,and providing the recommendation for an entity.

According to yet one or more example embodiments, a computer programproduct is provided. The computer program product can include a computerreadable storage medium having program instructions embodied therewith.The program instructions can be executable by a processor to cause theprocessor to: generate, by the processor, key performance indicatorsfrom a query, determine a subset of key performance indicators thatindividually have a performance below a threshold, and map the subset ofkey performance indicators to operational metrics. The computer programproduct further generates, by the processor, using an artificialintelligence, problem descriptions from one or more of the subset of keyperformance indicators or the operational metrics and transmit theproblem descriptions to a first database. The program also includesprogram instructions to retrieve, by the processor, the problemdescriptions from the first database, search a second database for arecommendation based on the problem descriptions, and provide therecommendation for an entity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a block diagram of an example, non-limiting system forproviding problem descriptions, in accordance with one or moreembodiments described herein.

FIG. 2 depicts an example architecture for the system and apparatusdisclosed herein, in accordance with one or more example embodimentsdescribed herein.

FIG. 3 depicts an example flow chart that describes aspects of theoperation of the disclosed system, in accordance with one or moreexample embodiments described herein.

FIG. 4 depicts an example of diagram that depicts training aspects ofthe system and associated artificial intelligence methods, in accordancewith one or more example embodiments described herein.

FIG. 5 depicts an example diagram that represents a deep-learningalgorithm component of for use in connection with the artificialalgorithms described herein, in accordance with one or more exampleembodiments described herein.

FIG. 6 depicts a diagram of an example flowchart for operating aspectsof the artificial intelligence based systems and algorithms used toprovide problem descriptions, in accordance with one or more exampleembodiments described herein.

FIG. 7 depicts a cloud computing environment, in accordance with one ormore embodiments described herein.

FIG. 8 depicts abstraction model layers, in accordance with one or moreembodiments described herein.

FIG. 9 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present disclosure are capable of being implementedin conjunction with any other type of computing environment now known orlater developed.

In some aspects, the terms “class,” “classification,” “decision,” or“recommendation” can be used interchangeably herein, unless specifiedotherwise.

In various embodiments, systems, methods, and apparatuses are describedfor automated problem description generation, for example, for use inconnection with technical checks of a system under test (e.g.,electronic devices and equipment). That is, embodiments of thedisclosure can be used to generate a description of a possible problemassociated with degradation of performance of a given system under test.Moreover, the systems, methods, and apparatuses described herein candetermine and use various operational metrics and/or key performanceindicators of the system under test for the automatic determination ofsuch problem descriptions, as will be described below.

In an aspect, embodiments of the disclosure can provide automaticproblem description generation for use in enterprise technical healthcheck operations, for example, technical health check operations oflarge-scale enterprise systems. In various aspects, the disclosed systemcan react to one or more user complaints (e.g., complaints in the formof electronic messages or requests). For example, the disclosed systemcan react to a user complaint about a performance of a database, aserver, or other components of a system under test. In another aspect,the system can react to such user complaints by dispatching arepresentative (e.g., an operator or maintenance individual) to visit auser site (for example, a factory, a medical facility, or other siteswhere the system under test is located). Such a representative candiagnose a problem with the systems according to the user's complaints.

Accordingly, the representative can fix the problem and/or provide oneor more recommendations for correcting the problem.

In one example embodiment, embodiments of the disclosure can providesurveys which can be sent to one or more technical architects in apredetermined time frame, for example, every 6-12 months. In anotherembodiment, these surveys can include various questions related to thesystem under test. For example, the survey can include one or morequestions to be answered on various topics or domains. In anotherembodiment, the topics or domains can include performance parameterssuch as server technology performance, hardware performance, malwareperformance, and the like. In an embodiment, the disclosed systems canprovide an operational, day-to-day data-driven approach for providing atechnical health check of a system and/or various components of a systemunder test using artificial intelligence, as will be described herein,thereby increasing the efficiency of the system under test and/or one ormore networks that the system under test is associated with (e.g., thecloud, local area networks (LANs), wide area networks (WANs), and thelike).

In an aspect, embodiments of the disclosure can automatically generateproblem descriptions using one or more key performance indicators thatindicate when there is a reduction in the performance of the systembeing monitored. In an embodiment, the disclosed system can analyze aquery from one or more users and classify the queries based at least inpart on the key performance indicators. In another embodiment, thesystem can find, in real time, one or more operational metricsassociated with the system under a test that can cause the particularkey performance indicators to indicate poor system performance. In oneembodiment, the disclosed system can generate one or more descriptionsof the key performance indicators and can also use the operationalmetrics based on the key performance indicators in order to create acontext associated with the key performance indicators and/oroperational metrics. Moreover, the context can include, for example,entities, categories, or topics associated with the key performanceindicators and/or operational metrics.

In an embodiment, the system can train a deep recurrent neural network(RNN) using the generated descriptions and/or context of the keyperformance indicators and/or the operational metrics. In oneembodiment, the key performance indicators and operational metrics aswell as other derived parameters can be synthesized to generate a singleproblem description or one or more problem descriptions, depending onthe characteristics of the system under test or one or more userpreferences.

In some aspects, the systems, methods, and apparatuses described hereincan be operable independent of the machine-learning classificationalgorithm used. In other words, any suitable supervised learningclassification algorithm can be used (including neural networks), makingthe disclosed embodiments widely deployable. For example, multiclass,multilabel machine-learning algorithms can be used and can include, butare not limited to, a support vector machine learning algorithm, anearest-neighbor machine-learning algorithm, a deep-learning algorithm,an extreme classification algorithm, a recursive learning algorithm, ahierarchical learning algorithm, a random forest algorithm, and thelike.

FIG. 1 illustrates a block diagram of an example, non-limiting system100 for providing artificial intelligence (AI) generated recommendationsalong with explanations, in accordance with one or more embodimentsdescribed herein. Aspects of systems (e.g., system 100 and the like),apparatuses, and/or processes described in this disclosure canconstitute machine-executable component(s) embodied within machine(s),e.g., embodied in one or more computer readable mediums (or media)associated with one or more machines. Such component(s), when executedby the one or more machines, e.g., computer(s), computing device(s),virtual machine(s), etc. can cause the machine(s) to perform theoperations described. Repetitive description of like elements employedin one or more embodiments described herein is omitted for sake ofbrevity.

System 100 can optionally include a server device, one or more networks,and one or more devices (not shown). The system 100 can also include orotherwise be associated with at least one processor 102 that executescomputer executable components stored in memory 104. The system 100 canfurther include a system bus 106 that can couple various componentsincluding, but not limited to, a query component 110, a learningcomponent 114, and a content component 116. The system 100 can be anysuitable computing device or set of computing devices that can becommunicatively coupled to devices, non-limiting examples of which caninclude, but are not limited to, a server computer, a computer, a mobilecomputer, a mainframe computer, an automated testing system, a networkstorage device, a communication device, a web server device, a networkswitching device, a network routing device, a gateway device, a networkhub device, a network bridge device, a control system, or any othersuitable computing device. A device can be any device that cancommunicate information with the system 100 and/or any other suitabledevice that can employ information provided by system 100. It is to beappreciated that system 100, components, models or devices can beequipped with communication component 118 that enable communicationbetween the system, components, models, devices, etc. over one or morenetworks.

In one embodiment, the query component 110 can provide contextualderivations from a user query. For example, the query component 110 cangenerate contextual derivations based on query analytics from one ormore virtual agents associated with the enterprise system or othersystem under test. In another embodiment, the query component 110 canprovide contextual derivations based on enterprise health controlindicators and/or key performance indicators that can be determined fromquery analytics and can be generated in an automatic or semi-automaticmanner.

In an embodiment, a content component 116 can derive, aggregate, and/orharvest information from one or more enterprise data sources in order toprovide an analysis of one or more issues or problems associated withthe system under test and can relate the issues to one or moreenterprise health controls. In an embodiment, the content component 116,can be a self-driven component. In one aspect, the content component 116can pull data from different sources; moreover, such data can includebut not limited to incidents, tickets, changes and inventory managementdata. In another aspect, the collected data may be categorized,transformed, and mapped to health controls, as described further below.In another aspect, the categorization and transformation of such datamay be done with AI-based algorithms (e.g. a classification algorithm toclassify tickets to incident categories).

In an embodiment, the learning component 114, can capture the contentand/or descriptions of the key performance indicators and/or operationalmetrics and can output a problem description using artificialintelligence, for example, using a deep-learning algorithm. In anembodiment, the learning component 114 can include an encoder-decoderstructure associated with the artificial intelligence (for example,associated with the deep-learning algorithms such as RNNs). In anembodiment, the encoder-decoder structure can be based on deep-learningtechnology, which can automatically generate problem statements from oneor more health control descriptions from the system under test (forexample, from the query component 110 and/or the content component 116).

In an embodiment, the query component 110, the content component 116,and/or the learning component 114 can work together or can exchange dataand information and/or instructions, such that these components canoptimize and accelerate the generation of problem descriptions from thequery analytics. As will be further described below, the system canfurther include one or more feedback mechanisms (e.g., based on a user'sinput, where the user can include a technical architect) to optimize theclassification of the user's queries to one or more key performanceindicators (that is, one or more feedback mechanisms to optimize themapping of the user's queries to one or more key performanceindicators). In an embodiment, the feedback mechanism can use a form ofactive learning. In another embodiment, a second feedback mechanism canbe used by the disclosed system (e.g., by the query component 110, thelearning component 114, and the content component 116) to optimize thegeneration of problem descriptions, for example, using active learning.In various embodiments, the system can thereby generate problemdescriptions in real time using operational data. Moreover, the systemand its various components can provide a proactive health check of asystem under test rather than a reactive technical health check that canlag the timing of the problems, and thereby provide real-time solutionsfor the system under test.

In an embodiment, the disclosed system (and its various components) canuse one or more generated problem descriptions to recommend solutions toresolve such problems, for example, by searching a solution database orthe Internet. Such solution databases can be provided on a database thatcan include a cloud-based environment, which is described further inconnection with FIG. 7 , below. In one or more embodiments, thedisclosed system can be used in connection with bots and/or automatedchat boxes that can automatically address a user's queries and/orquestions and integrate them with operational insights derived from theproblem descriptions and recommendations by the system. In anotherembodiment, the system (and its various components) can generalize thedescription of various problems' systems across other domains notrelated to technical health checks (for example, domains related tomedical diagnostics, internet search queries, and the like). In anembodiment, the disclosed system can enable the prediction of outages,failures, and can thereby identify revenue loss risks based on thedetection of a degradation of the one or more health checks and canprovide a resulting problem description and corresponding solution toactively reduce the incidence of such scenarios.

The various components (e.g., the query component 110, the contentcomponent 116, and/or the learning component 114, and/or othercomponents) can be connected either directly or via one or morenetworks. Such networks can include wired and wireless networks,including, but not limited to, a cellular network, a wide area network(WAN) (e.g., the Internet), or a local area network (LAN), non-limitingexamples of which include cellular, WAN, wireless fidelity (Wi-Fi),Wi-Max, WLAN, radio communication, microwave communication, satellitecommunication, optical communication, sonic communication, or any othersuitable communication technology. Moreover, the aforementioned systemsand/or devices have been described with respect to interaction betweenseveral components. It should be appreciated that such systems andcomponents can include those components or sub-components specifiedtherein, some of the specified components or sub-components, and/oradditional components. Sub-components could also be implemented ascomponents communicatively coupled to other components rather thanincluded within parent components. Further yet, one or more componentsand/or sub-components can be combined into a single component providingaggregate functionality. The components can also interact with one ormore other components not specifically described herein for the sake ofbrevity, but known by those of skill in the art.

Further, some of the processes performed can be performed by specializedcomputers for carrying out defined tasks related to various types ofneural networks in their particular context. The subject computerprocessing systems, methods apparatuses and/or computer program productscan be employed to solve new problems that arise through advancements intechnology, computer networks, the Internet and the like.

Embodiments of devices and systems (and their various components)described herein can employ artificial intelligence (AI) to facilitateautomating one or more features described herein (e.g., generatingproblem descriptions from queries and/or technical health checks). Thecomponents can employ various AI-based schemes for carrying out variousembodiments/examples disclosed herein. To provide for or aid in thenumerous determinations (e.g., determine, ascertain, infer, calculate,predict, prognose, estimate, derive, forecast, detect, compute)described herein, components described herein can examine the entiretyor a subset of the data to which it is granted access and can providefor reasoning about or determine states of the system, environment, etc.from a set of observations as captured via events and/or data.Determinations can be employed to identify a specific context or action,or can generate a probability distribution over states, for example. Thedeterminations can be probabilistic—that is, the computation of aprobability distribution over states of interest based on aconsideration of data and events. Determinations can also refer totechniques employed for composing higher-level events from a set ofevents and/or data.

Such determinations can result in the construction of new events oractions from a set of observed events and/or stored event data, whetherthe events are correlated in close temporal proximity, and whether theevents and data come from one or several event and data sources.Components disclosed herein can employ various classification(explicitly trained (e.g., via training data) as well as implicitlytrained (e.g., via observing behavior, preferences, historicalinformation, receiving extrinsic information, etc.)) schemes and/orsystems (e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines, etc.) inconnection with performing automatic and/or determined action inconnection with the claimed subject matter. Thus, classification schemesand/or systems can be used to automatically learn and perform a numberof functions, actions, and/or determinations.

A classifier can map an input attribute vector, z=(z1, z2, z3, z4, . . ., zn), to a confidence that the input belongs to a class, as byf(z)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determinate an action to be automaticallyperformed. A support vector machine (SVM) can be an example of aclassifier that can be employed. The SVM operates by finding ahyper-surface in the space of possible inputs, where the hyper-surfaceattempts to split the triggering criteria from the non-triggeringevents. Intuitively, this makes the classification correct for testingdata that is near, but not identical to training data. Other directedand undirected model classification approaches include, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and/or probabilistic classification models providing differentpatterns of independence can be employed. Classification as used hereinalso is inclusive of statistical regression that is utilized to developmodels of priority.

FIG. 2 shows an example architecture for the system and apparatusdisclosed herein, in accordance with one or more example embodiments ofthe disclosure. In particular, diagram 200 shows an architect 202; thearchitect can include a user of a database, a server, or other equipmentassociated with a facility or company (for example, an enterprisenetwork or server). In an embodiment, the architect 202 can interactwith a technical health check component 207 (which can be part of thequery component 110, described above in connection with FIG. 1 ). Thetechnical health check component 207 can include at least onequestionnaire 204 (to be filled out by the architect 202) or can includeone or more automatic computer checks 206. In an embodiment, thequestionnaire can include one or more inputs to the system from thearchitect 202 to provide a check of one or more operations of thesystem. In another embodiment, the system can provide automatic computerchecks 206 of the performance of the system under test. Moreover, theautomatic computer check 206 can be configured to perform similar, butnot necessarily identical, technical checks of the performance of agiven system, or some components of such a system as that provided bythe architect 202.

In an embodiment, the results of a questionnaire 204 and/or the computerautomatic checks 206 can be input to an intent classifier 208 (which canbe part of the query component 110, described in connection with FIG. 1, above). The intent classifier 208 can classify the context or canclassify the query originating from the questionnaire 204, or canclassify the results of the computer automatic checks 206 to one or morecontexts. In some aspects, the intent classifier 208 can include asupervised machine learning algorithm. For example, given previousqueries and their respective classes (e.g. database, mainframe, and/ormiddleware classes), a supervised machine learning algorithm may betrained and used to classify a new query into the respective classes,for example, in order to better understand the context of the query. Inone aspect, such a supervised machine learning algorithm may include asupport vector machine (SVM) algorithm or similar algorithms, or a deepneural network algorithm such as a convolutional neural network. Thecontext (for example, entities, categories, and/or topics) can beassociated with the results of the query and/or the computer automaticchecks 206.

In an embodiment, at block 205, the architect 202 can relate the queryto one or more key performance indicators, or the computer canautomatically relate the query to the key performance indicators. Inanother aspect, at 210, the output of the questionnaire 204 and/or thecomputer automatic checks 206 can be mapped to operational metrics, andthe system can identify the key performance indicators that have a lowmaturity score, meaning that they are indicative of problems that can beoccurring relatively quickly in time with respect to other systemproblems. For example, in one aspect, a look up table (or database) maybe generated and used to map a given automated check to the operationalmetrics. In an embodiment, such key performance indicators (e.g., thosehaving a low maturity score) can have increased urgency and can requirea rapid reaction or solution to the problem to prevent additionalmaintenance and/or outages in the system. In another embodiment, thesystem can identify key performance indicators that have a particularurgency associated with them.

At block 212, the key performance indicators and/or operational metricscan be received by a computational block 213, which can be part of thelearning component 114, described in connection with FIG. 1 , above. Inanother aspect, the key performance indicators and operationalparameters can be mapped by one another and can comprise a subset of theoriginal key performance indicators having either a low maturity scoreor having a high urgency score.

At block 214, the system can generate problem statements from theoperational metric description, which can be generated using one or moreartificial intelligence techniques to be described further below.

At block 218, the system can generate problem statements from the keyperformance indicator descriptions. Moreover, a storage unit and/or adatabase 217 can be used to provide information extraction or embeddingsand deep-learning decoding support (at block 216).

In an embodiment, the extracted information, the word embeddings, or thedeep-learning decoding resulting from block 216 can be used inconnection with operations of block 214 (for example, the generation ofproblem statements from operational metrics and/or descriptions).Additionally or alternatively, the information extraction or theembeddings and/or deep-learning decoding determined by block 216 can beused in connection with the operations of block 218 (e.g., in thegeneration of one or more problem statements from the key performanceindicator descriptions). In an embodiment, the output of operationalblocks 214 and/or 218 can be combined and used by the system, at block220, to synthesize and/or create problem descriptions.

In an embodiment, at block 220, the architect 202 can provide feedbackto better generate more accurate problem descriptions. In an embodiment,a key performance indicator correlation engine 222 (which can be part ofthe learning component 114, described in connection with FIG. 1 , above)can provide input to the operations of block 220. In an embodiment, thekey performance indicator correlation engine 222 can perform one or morecorrelations between a first subset of key performance indicators and asecond subset of key performance indicators. In another embodiment, thekey performance indicator correlation engine 222 can perform acorrelation between one subset of key performance indicators and asubset of operational metrics. In a third embodiment, the keyperformance indicator correlation engine 222 can perform a correlationbetween a subset of operational metrics and another subset ofoperational metrics.

At block 224, the system can use a recommendation search engine tosearch for recommendations based on the synthesized problem descriptiongenerated and created at block 220. In another aspect, therecommendation search engine can be part of the content component 116,described in connection with FIG. 1 , above. At block 228, the problemdescription and solution recommendation can be generated and provided,for example, to a user, including the architect 202.

FIG. 3 shows an example flow chart that describes aspects of theoperation of the disclosed system, in accordance with one or moreexample embodiments of the disclosure. At block 302, an input can bedetermined. In another aspect, the input can be determined at the querycomponent 110, described in connection with FIG. 1 , above. The input302 can include a technical architect's query 304, for example, a queryrelated to a poor performance of the system under test. In anotherembodiment (not shown), the input 302 can include the results from anautomated health check performed by the system or the system under test.In an embodiment, the technical architect's query 304 can result fromone or more inputs from the technical architect based on a problem withthe system and/or a component of the system under test (for example, areduced efficiency or operational capacity of the system under test).

At block 306, the system can classify the query and/or question and candetermine one or more key performance indicators based on the query. Inan embodiment, the classification can be performed using an intentclassifier, which can be part of the query component 110, described inconnection with FIG. 1 , above. In another embodiment, the system canmap the key performance indicators to one or more operational metrics.In an embodiment, the mapping of the key performance indicators and theoperational metrics can be performed by one or more artificialintelligence algorithms or can be performed using a table or a similarsearch algorithm.

In an embodiment at block 308, the system can recognize one or morepoorly performing key performance indicators. In an embodiment, thepoorly performing key performance indicators can include a keyperformance indicator having a score below a predetermined thresholdthat can be programmed by either a technical architect or a user of thesystem (for example, a given key performance indicator indicative of theefficiency of memory operations of the system can perform at a reducedspeed indicative of a poorly performing key performance indicator).

At block 310, the system can find one or more operational metrics thatcause the system to perform poorly (e.g., operational parameters thatare mapped to the key performance indicators determined in the previousoperational blocks). In an aspect, the determination of searchoperational metrics can be performed by the system using any suitablecomputer-based search, including a search of one or more tables. In anaspect, one or more operational insights 315 can be used to inform theoperational metrics at block 310. The operational insights 315 can beprovided by previous iterations of the operational flow 300, or can beextracted from inputs by a technical architect or users of the system.

At block 312, an artificial intelligence (AI) model 313 can generate aproblem description from operational metric descriptions. In one aspect,a supervised AI model may be used to generate the problem description.In particular, by using previous examples of operational metricsdescriptions (or some other operational related description) and theirmappings to problem descriptions, an AI-based neural network approach(e.g., an LSTM as shown and described in connection with FIG. 5 andrelated description) may be used to train the AI model forsequence-to-sequence learning. Such a model can be used to generate aproblem description for a given new operational metric. Alternatively oradditionally, an AI model 313 can generate problem descriptions from keyperformance indicators as shown in block 314. In another aspect, the AImodel 313 can be part of the learning component 114, described inconnection with FIG. 1 , above. In an aspect, training data 311 can beused to train the AI model 313. The training data 311 can include datathat has been previously determined and mapped to known key performanceindicators having defined characteristics (e.g., key performanceparameters that are poorly performing and mapped to known problemdescriptions and/or technical architect queries). In another aspect, thetraining data represents a finite set of information that can be used totrain the deep-learning model or other methods and/or artificialintelligence-based techniques implemented by the system. In anotheraspect, the training data can be stored on a database, for example, adatabase that is integrated into a cloud-computing environment, to bedescribed further below.

At block 320, the system can ask for feedback from the technicalarchitect to increase the quality of the training data 311. Thetechnical architect can, for example, provide an evaluation (e.g., ascore) corresponding to the accuracy of the model and training data 311.At block 316, the system can synthesize the problems and/or issues thatare based on different sources (e.g., on a subset of key performanceindicators, operational metrics, or the like). Alternatively oradditionally, the system can synthesize the problems from differentsubcomponents of a system under test from various operational insights,from various key performance indicators, from various operationalmetrics, and/or from one or more operational metric descriptions.

At block 318, the system can output the final problem description andcan request, at block 320, feedback from the technical architect inorder to approve the quality of the problem description generated atblock 318. In another aspect, the feedback can be incorporated into thecontent component 116, described in connection with FIG. 1 , above. Inanother aspect, the feedback can be provided in the form of aquestionnaire that can be displayed on a device such as a mobile device,and the technical architect can fill out the questionnaire. Further, atblock 322, the system can provide an output to a user and/or a technicalarchitect. For example, the system can provide a modified, simplifiedversion of the problem description 318 that can be easily understood orunderstandable by a person.

FIG. 4 shows an example of diagram 400 that depicts training aspects ofthe system and associated artificial intelligence methods, in accordancewith one or more example embodiments of the disclosure. In an aspect,the training of the system and associated artificial intelligencemethods can be performed as part of the learning component 114,described in connection with FIG. 1 , above. At block 402, a user canprovide labeled data and/or queries. In an embodiment, the labeled datacan be used as input to one or more labeled sets 408. Labeled data canrefer to data that has been previously classified and understood by theuser and can therefore be used as a benchmark to train the artificialintelligence techniques (e.g., deep-learning neural networks or thelike) used by the system. In another embodiment, the queries can be usedas unlabeled sets, by the system at block 404. In another aspect, theunlabeled sets can be used to test the efficacy of the trainedartificial intelligence techniques.

In particular, from a mathematical point of view, the learning component(e.g., the learning component 114, as described in connection with FIG.1 , above) can perform aspects of artificial intelligence training usinga multilabel label classifier, as follows. In an embodiment, the labeledset described above can be represented by D₁. In an embodiment, thesystem can train a multilabel label classifier, Fi, based on thetraining data D₁. In an embodiment, the system can predict, via modelprediction component 406, labels L₁, L₂ . . . L_(n) that correspond to agiven user inquiry. In an embodiment, the system can then ask a user tovalidate the generated labels L₁, L₂ . . . L_(n) for a given query cue.In an embodiment, the system can update the training data D₁ with theuser query cue and additional labels L_(i), L_(j), . . . L_(k). In anembodiment, the system can retrain a multi-label classifier F_(i) usingthe training data D_(i).

FIG. 5 shows an example diagram 500 that represents a deep-learningalgorithm component for use in connection with the artificial algorithmsdescribed herein, in accordance with one or more example embodiments ofdisclosure. At block 502, inputs can be provided to the system (e.g., atthe query component 110, described in connection with FIG. 1 , above),including a health control description 504. In an alternative embodiment(not shown), the input 502 can include queries from a user (such as atechnical architect), as variously described above. Moreover, the healthcontrol description 504 can be generated automatically using one or morecomputer-automated techniques implementing artificial intelligenceand/or can be generated manually (e.g., by a user filling, for example,questionnaires or via other forms of input 502 generated by the user).

At block 506, the system can extract information by an informationextraction engine 506 (e.g., at the query component 110, described inconnection with FIG. 1 , above), from the health control description504, or from other input 502 (e.g., user inputs from a questionnaire).At block 508, the system can generate (e.g., at the query component 110,described in connection with FIG. 1 , above) entities and relationshipsin triplet pairs: a triplet including an entity A, a relationship, andan entity B. In an embodiment, entity A can include a first component ofa system (e.g., a memory block). In another aspect, entity B can includean associated description of entity A or a parameter associated withentity A (e.g., memory rates, read/write rates, and the like). Inanother embodiment, the relationship relating entity A with entity B caninclude a description such as “reduce,” “increase,” or the like.Accordingly, the system can generate a description of a rudimentaryrelationship between entity A and entity B. In the cited example, thesystem can generate a description that a “memory block has a reducedread rate.”

At block 510, a word-embedding engine can be used to generate a wordmatrix 512. In an embodiment, the word matrix 512 can represent a matrixof words (e.g., word 1, word 2, . . . , word k) in various rows of thematrix, along with various entities (e.g., entity A1), relationships(e.g., relationship 1), and entities (e.g., entity B1) as the columns ofthe word matrix 512. The word matrix 512 can thereby capture variousword embeddings represented in the query and/or the health controldescriptions. In some aspects, the operations of block 510 can beperformed at the learning component 114, described in connection withFIG. 1 , above.

In an aspect, the output of the word matrix 512 can be fed into a neuralnetwork decoder 514 (e.g., performed at the learning component 114,described in connection with FIG. 1 , above). The neural network decoder514 can parse the words (for example, word 1, word 2, word 3 . . . wordN, represented by w₁, w₂, w₃ . . . w_(n)). Further, the neural networkdecoder 514 can use a deep-learning algorithm to determine theprobability of a given word following another word (for example, theneural network decoder 514 can use deep-learning to determine theprobability that a word 2 (e.g., w₂) follows word 2 (e.g., w₁) in adescription corresponding to user generated inquiry or a technicalhealth check. In an embodiment, the words (e.g., w₁, w₂, w₃ . . . w_(n))can be inputted to long short-term memory (LSTM) blocks, which cancompute the natural logarithm of the probability of the n-th word, thatis, log p(w_(n)), as part of the computations of the neural networkdecoder 514.

In an embodiment, the output of a first LSTM block (e.g., a first LSTMblock corresponding to entity A1) may be sequentially inputted to asubsequent LSTM block (e.g., a second LSTM block corresponding to therelationship), whose output may in turn be inputted to another LSTMblock (e.g., a third LSTM block corresponding to another entity B1). Inanother aspect, the output of the LSTM block corresponding to the entityB1 may be inputted to a subsequent LSTM block (e.g., a first LSTM blockcorresponding to word 1, w₁), whose output can be sequentially inputtedto a subsequent LSTM block (e.g., a second LSTM block corresponding toword 2, w₂), which can perform a similar operation, until the sequenceof words represented in the word matrix 512 is complete. In one aspect,the process described above may comprise a log probability (e.g., loglikelihood) for the i-th word (w_(i)) that corresponds to logp(w_(i))=log p(w₁, w₂, . . . , w_(i)|(Entity A, Relationship,Entity_(B))).

In an embodiment, the output of the neural network decoder 514 caninclude a predicted problem description 516, which can include asequence of predicted words (e.g., w₁, w₂, w₃ . . . w_(n)). Thepredicted words from the neural network decoder 518 can therebyrepresent the AI-algorithms predicted problem description thatcorresponds to the user's inquiry or results of the technical healthchecks that were originally inputted at block 502. In an embodiment, thepredicted problem description 516 can be outputted (e.g., at block 518,for example, to a user at a user display at a device (e.g., a mobiledevice) associated with the user (e.g., a technical architect).

FIG. 6 shows a flow chart of one or more operations for the disclosedsystem, in accordance with one or more example embodiments of thedisclosure. At block 602, a query component operatively coupled to aprocessor can generate key performance indicators from a query. Further,the query component can determine a subset of key performance indicatorsthat individually have a performance below a threshold. Further, thequery component can map the subset of key performance indicators tooperational metrics.

At block 604, a learning component operatively coupled to a processorcan generate, using artificial intelligence, problem descriptions fromone or more of the subset of key performance indicators or operationalmetrics. Further, the learning component can transmit the problemdescriptions to a first database.

At block 606, a content component operatively coupled to the processorcan retrieve the problem descriptions from the first database. Further,the content component can search a second database for a recommendationbased on the problem descriptions. Finally, the content component canprovide a recommendation based on the generated problem description to auser (for example, at a user device display).

As mentioned, one or more databases used in connection with thedisclosure can include a database stored or hosted on a cloud computingplatform. It is to be understood that although this disclosure includesa detailed description on cloud computing, implementation of theteachings recited herein are not limited to a cloud computingenvironment. Rather, embodiments of the present invention are capable ofbeing implemented in conjunction with any other type of computingenvironment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model can includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but can be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It can be managed by the organization or a third party andcan exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It can be managed by the organizations or a third partyand can exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 7 , illustrative cloud computing environment 700is depicted. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. As shown,cloud computing environment 700 includes one or more cloud computingnodes 702 with which local computing devices used by cloud consumers,such as, for example, personal digital assistant (PDA) or cellulartelephone 704, desktop computer 706, laptop computer 708, and/orautomobile computer system 710 can communicate. Nodes 702 cancommunicate with one another. They can be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 700 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 704-710shown in FIG. 7 are intended to be illustrative only and that computingnodes 702 and cloud computing environment 700 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 8 , a set of functional abstraction layersprovided by cloud computing environment 700 (FIG. 7 ) is shown.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity. It should be understoodin advance that the components, layers, and functions shown in FIG. 7are intended to be illustrative only and embodiments of the inventionare not limited thereto. As depicted, the following layers andcorresponding functions are provided.

Hardware and software layer 802 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 804;RISC (Reduced Instruction Set Computer) architecture-based servers 806;servers 808; blade servers 810; storage devices 812; and networks andnetworking components 814. In some embodiments, software componentsinclude network application server software 816 and database software818.

Virtualization layer 820 provides an abstraction layer from which thefollowing examples of virtual entities can be provided: virtual servers822; virtual storage 824; virtual networks 826, including virtualprivate networks; virtual applications and operating systems 828; andvirtual clients 830.

In one example, management layer 832 can provide the functions describedbelow. Resource provisioning 834 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 836provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources can include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 838 provides access to the cloud computing environment forconsumers and system administrators. Service level management 840provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 842 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 844 provides examples of functionality for which thecloud computing environment can be utilized. Examples of workloads andfunctions which can be provided from this layer include: mapping andnavigation 846; software development and lifecycle management 848;virtual classroom education delivery 850; data analytics processing 852;transaction processing 854; and assessing an entity's susceptibility toa treatment service 856. Various embodiments of the present inventioncan utilize the cloud computing environment described with reference toFIGS. 7 and 8 to determine problem descriptions based on training dataand/or databases associated with key performance indicators oroperational metrics as variously described herein.

The present invention can be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product can include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium can be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions can be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionscan also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 9 as well as the following discussion are intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. FIG.9 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. Withreference to FIG. 9 , a suitable operating environment 900 forimplementing various aspects of this disclosure can include a computer912. The computer 912 can also include a processing unit 914, a systemmemory 916, and a system bus 918. The system bus 918 can operably couplesystem components including, but not limited to, the system memory 916to the processing unit 914. The processing unit 914 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit914. The system bus 918 can be any of several types of bus structuresincluding the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire, and Small ComputerSystems Interface (SCSI). The system memory 916 can also includevolatile memory 920 and nonvolatile memory 922. The basic input/outputsystem (BIOS), containing the basic routines to transfer informationbetween elements within the computer 912, such as during start-up, canbe stored in nonvolatile memory 922. By way of illustration, and notlimitation, nonvolatile memory 922 can include read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 920 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 912 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 9 illustrates, forexample, a disk storage 924. Disk storage 924 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 924 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 924 to the system bus 918, a removable ornon-removable interface can be used, such as interface 926. FIG. 9 alsodepicts software that can act as an intermediary between users and thebasic computer resources described in the suitable operating environment900. Such software can also include, for example, an operating system928. Operating system 928, which can be stored on disk storage 924, actsto control and allocate resources of the computer 912. Systemapplications 930 can take advantage of the management of resources byoperating system 928 through program components 932 and program data934, e.g., stored either in system memory 916 or on disk storage 924. Itis to be appreciated that this disclosure can be implemented withvarious operating systems or combinations of operating systems. A userenters commands or information into the computer 912 through one or moreinput devices 936. Input devices 936 can include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices can connect to the processing unit914 through the system bus 918 via one or more interface ports 938. Theone or more Interface ports 938 can include, for example, a serial port,a parallel port, a game port, and a universal serial bus (USB). One ormore output devices 940 can use some of the same type of ports as inputdevice 936. Thus, for example, a USB port can be used to provide inputto computer 912, and to output information from computer 912 to anoutput device 940. Output adapter 942 can be provided to illustrate thatthere are some output devices 940 like monitors, speakers, and printers,among other output devices 940, which require special adapters. Theoutput adapters 942 can include, by way of illustration and notlimitation, video and sound cards that provide a means of connectionbetween the output device 940 and the system bus 918. It should be notedthat other devices and/or systems of devices provide both input andoutput capabilities such as one or more remote computers 944.

Computer 912 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer944. The remote computer 944 can be a computer, a server, a router, anetwork PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 912.For purposes of brevity, only a memory storage device 946 is illustratedwith remote computer 944. Remote computer 944 can be logically connectedto computer 912 through a network interface 948 and then physicallyconnected via communication connection 950. Further, operation can bedistributed across multiple (local and remote) systems. Networkinterface 948 can encompass wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL). One or morecommunication connections 950 refers to the hardware/software employedto connect the network interface 948 to the system bus 918. Whilecommunication connection 950 is shown for illustrative clarity insidecomputer 912, it can also be external to computer 912. Thehardware/software for connection to the network interface 948 can alsoinclude, for exemplary purposes only, internal and external technologiessuch as, modems including regular telephone grade modems, cable modemsand DSL modems, ISDN adapters, and Ethernet cards.

Embodiments of the present invention can be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention. The computer readable storage mediumcan be a tangible device that can retain and store instructions for useby an instruction execution device. The computer readable storage mediumcan be, for example, but is not limited to, an electronic storagedevice, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can includecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of various aspects of thepresent invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to customize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein includes an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, component, segment,or portion of instructions, which includes one or more executableinstructions for implementing the specified logical function(s). In somealternative implementations, the functions noted in the blocks can occurout of the order noted in the Figures. For example, two blocks shown insuccession can, in fact, be executed substantially concurrently, or theblocks can sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules or components. Generally, programmodules or components include routines, programs, components, datastructures, etc. that perform particular tasks and/or implementparticular abstract data types. Moreover, those skilled in the art willappreciate that the inventive computer-implemented methods can bepracticed with other computer system configurations, includingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as computers, hand-held computingdevices (e.g., PDA, phone), microprocessor-based or programmableconsumer or industrial electronics, and the like. The illustratedaspects can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. However, some, if not all aspects ofthis disclosure can be practiced on stand-alone computers. In adistributed computing environment, program modules or components can belocated in both local and remote memory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or deviceincluding, but not limited to, single-core processors; single-processorswith software multithread execution capability; multi-core processors;multi-core processors with software multithread execution capability;multi-core processors with hardware multithread technology; parallelplatforms; and parallel platforms with distributed shared memory.Additionally, a processor can refer to an integrated circuit, anapplication specific integrated circuit (ASIC), a digital signalprocessor (DSP), a field programmable gate array (FPGA), a programmablelogic controller (PLC), a complex programmable logic device (CPLD), adiscrete gate or transistor logic, discrete hardware components, or anycombination thereof designed to perform the functions described herein.Further, processors can exploit nano-scale architectures such as, butnot limited to, molecular and quantum-dot based transistors, switchesand gates, in order to optimize space usage or enhance performance ofuser equipment. A processor can also be implemented as a combination ofcomputing processing units. In this disclosure, terms such as “store,”“storage,” “data store,” data storage,” “database,” and substantiallyany other information storage component relevant to operation andfunctionality of a component are utilized to refer to “memorycomponents,” entities embodied in a “memory,” or components including amemory. It is to be appreciated that memory and/or memory componentsdescribed herein can be either volatile memory or nonvolatile memory, orcan include both volatile and nonvolatile memory. By way ofillustration, and not limitation, nonvolatile memory can include readonly memory (ROM), programmable ROM (PROM), electrically programmableROM (EPROM), electrically erasable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory can include RAM, which can act as external cache memory,for example. By way of illustration and not limitation, RAM is availablein many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), direct Rambus RAM (DRRAM),direct Rambus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM).Additionally, the disclosed memory components of systems orcomputer-implemented methods herein are intended to include, withoutbeing limited to including, these and any other suitable types ofmemory.

What has been described above include mere examples of systems, computerprogram products and computer-implemented methods. It is, of course, notpossible to describe every conceivable combination of components,products and/or computer-implemented methods for purposes of describingthis disclosure, but one of ordinary skill in the art can recognize thatmany further combinations and permutations of this disclosure arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim. The descriptions of thevarious embodiments have been presented for purposes of illustration,but are not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen tobest explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer-executable components; a processor, operably coupled to thememory, and that executes the computer-executable components stored inthe memory, wherein the computer-executable components comprise: a querycomponent that generates contextual derivations from key performanceindicators based on a query associated with a system under test; alearning component that uses an artificial intelligence (AI) model togenerate problem descriptions from the key performance indicators,wherein training data used to train the AI model comprises informationassociated with known key performance indicators comprising keyperformance parameters mapped to known problem descriptions; and acontent component that provides a recommendation to an entity, based onthe problem descriptions, at a display device associated with theentity, wherein the entity uses the recommendation to correct a problemassociated with the system under test.
 2. The system of claim 1, whereinthe learning component further generates the problem descriptions bycomputing at least one of: one or more correlations between a firstsubset of the key performance indicators and a second subset of the keyperformance indicators, a correlation between a subset of the keyperformance indicators and a subset of operational metrics, or acorrelation between a first subset of the operational metrics and asecond subset of the operational metrics.
 3. The system of claim 1,wherein the content component derives information from one or moreenterprise data sources to provide an analysis of one or more problemsassociated with the system under test.
 4. The system of claim 3, whereinthe content component relates the one or more problems to one or moreenterprise health controls.
 5. The system of claim 1, wherein the AImodel includes a recurrent neural network.
 6. The system of claim 5,wherein the recurrent neural network comprises a long-short term memoryneural network.
 7. The system of claim 1, wherein the query componentcomputes the key performance indicators by classifying the query using amulti-label classifier.
 8. The system of claim 7, wherein a user trainsthe multi-label classifier by validating labels predicted by themulti-label classifier for training data comprising labeled queries andunlabeled queries.
 9. The system of claim 1, wherein the query componentcomputes the key performance indicators in an automatic manner or asemi-automatic manner, and wherein the generating the problemdescriptions provides a real-time health check of the system under test.10. A computer-implemented method, comprising: generating, by a systemoperatively coupled to a processor, contextual derivations from keyperformance indicators based on a query associated with a system undertest; generating, by the system, using an artificial intelligence (AI)model, problem descriptions from the key performance indicators, whereintraining data used to train the AI model comprises informationassociated with known key performance indicators comprising keyperformance parameters mapped to known problem descriptions; andproviding, by the system, a recommendation to an entity at a displaydevice associated with the entity, based on the problem descriptions,wherein the entity uses the recommendation to correct a problemassociated with the system under test.
 11. The computer-implementedmethod of claim 10, further comprising: generating, by the system, theproblem descriptions by computing at least one of: one or morecorrelations between a first subset of the key performance indicatorsand a second subset of the key performance indicators, a correlationbetween a subset of the key performance indicators and a subset ofoperational metrics, or a correlation between a first subset of theoperational metrics and a second subset of the operational metrics. 12.The computer-implemented method of claim 10, further comprising:deriving, by the system, information from one or more enterprise datasources to provide an analysis of one or more problems associated withthe system under test and relate the one or more problems to one or moreenterprise health controls.
 13. The computer-implemented method of claim10, wherein the AI model includes a recurrent neural network, andwherein the recurrent neural network comprises a long-short term memoryneural network.
 14. The computer-implemented method of claim 10, furthercomprising: computing, by the system, the key performance indicators byclassifying the query using a multi-label classifier.
 15. Thecomputer-implemented method of claim 14, wherein the multi-labelclassifier is trained by a user by validating labels predicted by themulti-label classifier for training data comprising labeled queries andunlabeled queries.
 16. A computer program product for automated problemgeneration using artificial intelligence, comprising a computer readablestorage medium having program instructions embodied therewith, theprogram instructions executable by a processor to cause the processorto: generate, by the processor, contextual derivations from keyperformance indicators based on a query associated with a system undertest; generate, by the processor, using an artificial intelligence (AI)model, problem descriptions from the key performance indicators, whereintraining data used to train the AI model comprises informationassociated with known key performance indicators comprising keyperformance parameters mapped to known problem descriptions; andprovide, by the processor, a recommendation to an entity at a displaydevice associated with the entity, based on the problem descriptions,wherein the entity uses the recommendation to correct a problemassociated with the system under test.
 17. The computer program productof claim 16, wherein the program instructions are further executable bythe processor to cause the processor to: generate, by the processor, theproblem descriptions by computing at least one of: one or morecorrelations between a first subset of the key performance indicatorsand a second subset of the key performance indicators, a correlationbetween a subset of the key performance indicators and a subset ofoperational metrics, or a correlation between a first subset of theoperational metrics and a second subset of the operational metrics. 18.The computer program product of claim 16, wherein the programinstructions are further executable by the processor to cause theprocessor to: derive, by the processor, information from one or moreenterprise data sources to provide an analysis of one or more problemsassociated with the system under test and relate the one or moreproblems to one or more enterprise health controls.
 19. The computerprogram product of claim 16, wherein the AI model includes a recurrentneural network, and wherein the recurrent neural network comprises along-short term memory neural network.
 20. The computer program productof claim 16, wherein the program instructions are further executable bythe processor to cause the processor to: compute, by the processor, thekey performance indicators by classifying the query using a multi-labelclassifier.
 21. A system, comprising: a memory that storescomputer-executable components; a processor, operably coupled to thememory, and that executes the computer-executable components stored inthe memory, wherein the computer-executable components comprise: a querycomponent that generates contextual derivations from key performanceindicators based on a query associated with a system under test; alearning component that uses an artificial intelligence (AI) model togenerate problem descriptions from the key performance indicators bycomputing one or more correlations between a first subset of the keyperformance indicators and a second subset of the key performanceindicators, wherein training data used to train the AI model comprisesinformation associated with known key performance indicators comprisingkey performance parameters mapped to known problem descriptions; and acontent component that provides a recommendation to an entity, based onthe problem descriptions, at a display device associated with theentity, wherein the entity uses the recommendation to correct a problemassociated with the system under test.
 22. The system of claim 21,wherein the AI model includes a recurrent neural network, and whereinthe recurrent neural network comprises a long-short term memory neuralnetwork.
 23. The system of claim 21, wherein the query componentcomputes key performance indicators by classifying the query using amulti-label classifier.
 24. A computer-implemented method, comprising:generating, by a system operatively coupled to a processor, contextualderivations from key performance indicators based on a query associatedwith a system under test; generating, by the system, using an artificialintelligence (AI) model, problem descriptions from the key performanceindicators by computing one or more correlations between a first subsetof the key performance indicators and a second subset of the keyperformance indicators, wherein training data used to train the AI modelcomprises information associated with known key performance indicatorscomprising key performance parameters mapped to known problemdescriptions; and providing, by the system, a recommendation to anentity at a display device associated with the entity, based on theproblem descriptions, wherein the entity uses the recommendation tocorrect a problem associated with the system under test.
 25. Thecomputer-implemented method of claim 24, further comprising: computing,by the system, the key performance indicators by classifying the queryusing a multi-label classifier.